Occupant localization using footstep-induced structural vibration

Mirshekari, Mostafa; Pan, Shijia; Fagert, Jonathon; Schooler, Eve M.; Zhang, Pei; Noh, Hae Young

doi:10.1016/j.ymssp.2018.04.026

Cited by 102 publications

(68 citation statements)

References 67 publications

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“…When exceedance of the detection thresholds occurs at one sensor, a footstep event is detected and a signal window (length equal to duration of a single footstep) is extracted at all sensor locations. Each footstep event signal is processed using wavelet decomposition (Ford, 2003;Mirshekari et al, 2018) in order to explore their low-and-high frequency components. Morlet wavelet (Lin and Qu, 2000) is used as the mother wavelet in the continuous wavelet transform (CWT) due to its shape similarity to the footstep impact signal.…”

Section: Methodsmentioning

confidence: 99%

“…Footstep-induced floor vibrations are characterized as nonstationary signals (Mirshekari et al, 2018). Thus, CWT is proposed to compare footstep impact signals with a mother wavelet based on various scaling in order to decompose the signal in the time domain.…”

Section: Vibration-measurement Analysismentioning

confidence: 99%

“…To date, the most common strategy has been to analyze signals in the absence of a structural behavior model. Such model-free approaches involve processing and analyzing vibration measurements and require at least three sensors with overlapping coverage where occupants could be localized (Mirshekari et al, 2018). In other studies, dispersion observed for wave propagation through the floor as well as low signal-to-noise ratio of footstep-induced floor vibrations have led to distorted signals and uncertain localization results.…”

Section: Introductionmentioning

confidence: 99%

“…Thus, additional sensors were required to increase the accuracy of occupant detection and localization (Bahroun et al, 2014;Pan et al, 2014Pan et al, , 2015Pan et al, , 2016. Moreover, complex structural configurations that contained structural and non-structural elements such as walls and beams have limited the applicability of model-free occupant localization approaches (Mirshekari et al, 2018). Such obstructions affected the wave propagation, thus resulting in a source of ambiguity that compromised occupant localization.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Model-Based Occupant Tracking Using Slab-Vibration Measurements

Drira

Reuland

Pai

et al. 2019

Front. Built Environ.

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Sensor-based occupant tracking has the potential to enhance knowledge of the utilization of buildings. Occupancy-tracking strategies using footstep-induced floor vibrations may be beneficial for thermal-load prediction, security enhancement, and care-giving without undermining privacy. Current floor-vibration-based occupant-tracking methodologies are based on data-driven techniques that do not include a physics-based model of the structural behavior of the floor slab. These techniques suffer from ambiguous interpretations when signals are affected by complex configurations of structural and non-structural elements such as beams and walls. Using a physics-based model for data-interpretation enables deployment of sparse number of sensors in contexts of non-uniform structural configurations. In this paper, an application of physics-based data interpretation using error-domain model falsification (EDMF) is presented to track an occupant within an office environment through footstep-induced floor vibrations. EDMF is a population-based approach that incorporates various sources of uncertainty, including bias, arising from measurements and modeling. EDMF involves the rejection of simulated model responses that contradict footstep-induced floor vibration measurements. Thus, EDMF provides a set of candidate locations from an initial population of possible occupant locations. A sequential analysis that accommodates information from previous footsteps is then used to enhance candidate locations and identify trajectories among candidates. In this way, incorporating structural behavior in interpreting vibration measurements induced by occupant footsteps has the potential to identify accurately the trajectory of an occupant in buildings with complex configurations, thereby providing tracking information without undermining privacy.

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Section: Methodsmentioning

confidence: 99%

Section: Vibration-measurement Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Model-Based Occupant Tracking Using Slab-Vibration Measurements

Drira

Reuland

Pai

et al. 2019

Front. Built Environ.

Self Cite

View full text Add to dashboard Cite

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“…Related research in the literature [8][9][10] on indoor footstep localization is in the early stages, and a small number of reports are available [8]. These prior publications were motivated by indoor occupant localization for energy management, understanding occupant behavior, facility security, and other smart building applications.…”

Section: Related Literaturementioning

confidence: 99%

Classification of Inter-Floor Noise Type/Position Via Convolutional Neural Network-Based Supervised Learning

et al. 2019

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Inter-floor noise, i.e., noise transmitted from one floor to another floor through walls or ceilings in an apartment building or an office of a multi-layered structure, causes serious social problems in South Korea. Notably, inaccurate identification of the noise type and position by human hearing intensifies the conflicts between residents of apartment buildings. In this study, we propose a robust approach using deep convolutional neural networks (CNNs) to learn and identify the type and position of inter-floor noise. Using a single mobile device, we collected nearly 2000 inter-floor noise events that contain 5 types of inter-floor noises generated at 9 different positions on three floors in a Seoul National University campus building. Based on pre-trained CNN models designed and evaluated separately for type and position classification, we achieved type and position classification accuracy of 99.5% and 95.3%, respectively in validation datasets. In addition, the robustness of noise type classification with the model was checked against a new test dataset. This new dataset was generated in the building and contains 2 types of inter-floor noises at 10 new positions. The approximate positions of inter-floor noises in the new dataset with respect to the learned positions are presented.

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